Efficient Regularized Regression with Penalty for Variable Selection and Network Construction

Table 5

Performance measures for regularized regression for graphical structure detection over 100 simulations, where values in the parenthesis are the standard deviations.

	Band 1			Band 2
AIC	AUC	FDR ()	FNR ()	AUC	FDR ()	FNR ()
	.95 (±.01)	.29 (±.08)	9.4 (±2.6)	.82 (±.01)	.10 (±.05)	36.7 (±1.5)
100	.99 (±.005)	.20 (±.06)	1.2 (±1.1)	.84 (±.01)	.11 (±.04)	32.7 (±1.9)
200	.999 (±.0003)	.20 (±.05)	0 (±0)	.93 (±.01)	.11 (±.04)	14.2 (±2.4)
BIC	AUC	FPR (%)	FNR (%)	AUC	FPR (%)	FNR (%)
	.90 (±.02)	.10 (±.05)	20 (±3.6)	.803 (±.008)	.02 (±.02)	39.3 (±1.5)
100	.991 (±.007)	.03 (±.03)	1.8 (±1.3)	.83 (±.01)	.03 (±.02)	34.9 (±1.6)
200	.9999 (±.0005)	.01 (±.01)	.01 (±.10)	.82 (±.01)	.03 (±.02)	36.7 (±1.8)
	AUC	FPR (%)	FNR (%)	AUC	FPR (%)	FNR (%)
	.91 (±.03)	3.5 (±.05)	11 (±3.6)	0.77 (±.01)	5.3 (±.07)	40.9 (±.62)
100	.99 (±.003)	1.52 (±.22)	.33 (±.67)	0.78 (±.007)	7.1 (±1.4)	36.3 (±1.1)
200	.99 (±.003)	1.21 (±.07)	.45 (±.53)	0.79 (±.01)	8.1 (±.57)	34.0 (±1.4)